A Risk-Based Bi-Level Bidding System to Manage Day-Ahead Electricity Market and Scheduling of Interconnected Microgrids in the presence of Smart Homes

Abstract

This paper presents a bi-level bidding system for managing energy exchange between interconnected microgrids in the presence of traditional and smart consumers, in which the conditional value at risk (CVaR) method is employed to manage the risk arising from uncertainties of load demand and renewable generations (RGs). In the upper level of the proposed model, the microgrids create their offers/bids and send them to the community manager. Then in the lower level, the community manager sets the market-clearing price with the aim of maximizing social welfare. The studied market consists of three microgrids, each of which covers regular and smart consumers. Smart consumers control their appliances through the internet of things (IoT) concept, and regular consumers are able to participate in an incentive-based demand response program (DRP). The proposed model is formulated in mixed-integer quadratic programming (MIQCP) format and solved by GUROBI solver in the GAMS environment. The simulation results demonstrate that risk-taker scheduling not only reduces the market-clearing price but also increases the comfort index of smart consumers. Also, the results illustrate that modifying the consumption pattern of regular consumers through the DRP leads to more available power during peak hours and increases the comfort index of smart consumers.